Quantum Information Processing最新文献

The syndrome decoding problem is known to be NP-complete. The goal of the decoder is to find an error of low weight that corresponds to a given syndrome obtained from a parity-check matrix. We use the quantum approximate optimization algorithm (QAOA) to address the syndrome decoding problem with elegantly designed reward Hamiltonians based on both generator and check matrices for classical and quantum codes. We evaluate the level-4 check-based QAOA decoding of the [7,4,3] Hamming code, as well as the level-4 generator-based QAOA decoding of the [[5,1,3]] quantum code. Remarkably, the simulation results demonstrate that the decoding performances match those of the maximum-likelihood decoding. Moreover, we explore the possibility of enhancing QAOA by introducing additional redundant clauses to a combinatorial optimization problem while keeping the number of qubits unchanged. Finally, we study QAOA decoding of degenerate quantum codes. Typically, conventional decoders aim to find a unique error of minimum weight that matches a given syndrome. However, our observations reveal that QAOA has the intriguing ability to identify degenerate errors of comparable weight, providing multiple potential solutions that match the given syndrome with comparable probabilities. This is illustrated through simulations of the generator-based QAOA decoding of the [[9,1,3]] Shor code on specific error syndromes.

Quantum energy teleportation (QET) protocol illustrates that through local operations and classical communication, the local energy of the ground state of a many-body quantum system can be extracted. Unlike classical energy transmission, dissipation effects are greatly reduced in quantum energy teleportation. Energy extraction only requires classical information and local operations about the measurements. Quantum resources play a key role in this protocol, giving QET protocol quantum advantages over classical energy transmission. In this paper, we investigate the role of quantum resources in quantum energy teleportation. We find that quantum resources can improve the energy extraction efficiency of QET, and find the necessary and sufficient conditions for the minimal QET. We construct a quantum circuit for simulation of the minimal QET model and provide the numerical results of QET in Gibbs state and spin-chain system to verify our conclusions.

We consider a two-mode bosonic state with fixed photon number (n in mathbb {N}), whose upper and lower modes pick up a phase (phi ) and (-phi ), respectively. We compute the optimal Fock coefficients of the input state, such that the mean square error (MSE) for estimating (phi ) is minimized, while the minimum MSE is always attainable by a measurement. Our setting is Bayesian, i.e., we consider (phi ) to be a random variable that follows a prior probability distribution function (PDF). Initially, we consider the flat prior PDF and we discuss the well-known fact that the MSE is not an informative tool for estimating a phase when the variance of the prior PDF is large. Therefore, we move on to study truncated versions of the flat prior in both single-shot and adaptive approaches. For our adaptive technique, we consider (n=1) and truncated prior PDFs. Each subsequent step utilizes as prior PDF the posterior probability of the previous step, and at the same time we update the optimal state and optimal measurement.

Based on the study of quantum dialogue and quantum identity authentication, this paper proposes an improved three-party quantum dialogue (3P-QD) with dual authentication of identity protocol based on quantum search algorithm (QSA) and bidirectional verification of user identity. The protocol utilizes two-particle states as quantum resources to achieve bidirectional transmission of information in the channel and uses the non-cloning theorem of quantum mechanics, which ensures that sequences of quantum bits can be reliably and securely guaranteed during transmission. The characteristics of the QSA in the two-qubit are applied to the target state search process of this 3P-QD protocol, which can accomplish the task of safely transferring two bits of information between the two communicating parties. In addition, in the communication, one of the fixed third party is not only a communicating party, but also acts as a controller for the other two parties. Meanwhile, in order to ensure the security and integrity of the communication, a bidirectional authentication step is added to the communication process, which well solves the problem of eavesdropping. Based on the above features, compared with the existing protocols, this protocol has better advantages.

In this paper, we discuss how to reduce the interference that noise introduces into the scalar input signal of a quantum gate. Non-separable quantum gates can be made by making a small potential change to the Hamiltonian and then using perturbation theory to figure out the evolution operator. It is assumed that a scalar, temporally varying signal modulates the potential. To lessen the impact of noise on the design of the gate, we here take into account an extra noise component in the input signal and process it with a linear time-invariant filter. In order to meet these requirements, the Frobenius norm of the difference between the realized gate and the theoretical gate is minimized while taking into account the energy of the signal and the energy of the filter. Results from a computer simulation have been obtained by discretizing the resulting equations. The simulation results show that the proposed method effectively reduces the impact of noise on the gate design and improves its performance. This approach can be useful in designing gates for various applications, including signal processing and communication systems.

Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of the network to reduce computational complexity and mitigate overfitting. However, this operation often results in a significant loss of information, which can affect the performance of classification models. To overcome this limitation, we introduce a novel image classification framework that leverages variational quantum algorithms (VQAs) hybrid approaches combining quantum and classical computing paradigms within quantum machine learning. The major advantage of our framework is the elimination of the need for the global pooling operation at the end of the network. In this way, our approach preserves more discriminative features and fine-grained details in the images, which enhances classification performance. Additionally, employing VQAs enables our framework to have fewer parameters than the classical framework, even in the absence of global pooling, which makes it more advantageous in preventing overfitting. We apply our method to different state-of-the-art image classification models and demonstrate the superiority of the proposed quantum architecture over its classical counterpart through a series of state vector simulation experiments on public datasets. Our experiments show that the proposed quantum framework achieves up to a 9.21% increase in accuracy and up to a 15.79% improvement in F1 score, compared to the classical framework. Additionally, we explore the impact of shot noise on our method through shot-based simulation and find that increasing the number of measurements does not always lead to better results. Selecting an appropriate number of measurements can yield optimal results, even surpassing those obtained from state vector simulation.

A binary constant weight code is a type of error-correcting code with a wide range of applications. The problem of finding a binary constant weight code has long been studied as a combinatorial optimization problem in coding theory. In this paper, we propose a quantum search algorithm for binary constant weight codes. Specifically, the search problem is formulated as a polynomial binary optimization problem and Grover adaptive search is used for providing the quadratic speedup. Focusing on the inherent structure of the problem, we derive an upper bound on the minimum of the objective function value and a lower bound on the exact number of solutions. By exploiting these two bounds, we successfully reduced the constant overhead of the algorithm, although the overall query complexity remains exponential due to the NP-complete nature of the problem. In our algebraic analysis, it was found that this proposed algorithm is capable of reducing the number of required qubits, thus enhancing the feasibility. Additionally, our simulations demonstrated that it reduces the average number of classical iterations by 63% as well as the average number of total Grover rotations by 31%. The proposed approach may be useful for other quantum search algorithms and optimization problems.

Recently, Yuan et al. (Phys Rev A 102:042228, 2020) construct a set of strongly nonlocal orthogonal product states in (mathbb {C}^{d} otimes mathbb {C}^{d} otimes mathbb {C}^{d}), where (d ge 3). Then, it is interesting how much entanglement resource is sufficient to locally distinguish these states, and very little is known about entanglement-assisted distinguishing strongly nonlocal quantum states. In this paper, we first design one entanglement-assisted state discrimination protocols for strongly nonlocal orthogonal product states in (mathbb {C}^{4} otimes mathbb {C}^{4} otimes mathbb {C}^{4}) quantum system. Then, in the same way, we give how much entanglement resource is sufficient to locally distinguish these strongly nonlocal orthogonal product states in (mathbb {C}^{5} otimes mathbb {C}^{5} otimes mathbb {C}^{5}), (mathbb {C}^{6} otimes mathbb {C}^{6} otimes mathbb {C}^{6}) quantum systems. Finally, we propose a conjecture that less entanglement resource than quantum teleportation can be used to locally distinguish these strongly nonlocal quantum states in (mathbb {C}^{d} otimes mathbb {C}^{d} otimes mathbb {C}^{d}), where (d ge 3). The above results can illustrate the important role of entanglement resource in locally distinguishing orthogonal quantum states and also reveal the phenomenon of less nonlocality with more entanglement.

Irreversible entropy production (IEP) plays an important role in the field of quantum thermodynamics. In the present work, we investigate the geometrical bounds of IEP in nonequilibrium thermodynamics by exemplifying a two-qubit system coupled to three noise channels, including amplitude damping channel, phase damping channel, and depolarizing channel, respectively. We find that the geometrical bounds of the IEP always shift in an identical way, namely, only the upper bound becomes tighter under phase damping channel and depolarizing channel, respectively, in the presence of correlation effect of the noise channel. However, both the lower bound and the upper bound turn to be tighter in the situation of amplitude damping channel in the presence of correlation effect of the noise channel. By harvesting the benefits of correlation effect of noise channel and the entanglement between two qubits, the values of the IEP, quantifying the degree of thermodynamic irreversibility, could be suppressed in a controllable manner. Our results are expected to deepen our understanding of the nature of irreversibility under ambient conditions.

We present an experimental scanning-based tomography approach for near-term quantum devices. The underlying method has previously been introduced in an ensemble-based NMR setting. Here we provide a tutorial-style explanation along with suitable software tools to guide experimentalists in its adaptation to near-term pure-state quantum devices. The approach is based on a Wigner-type representation of quantum states and operators. These representations provide a rich visualization of quantum operators using shapes assembled from a linear combination of spherical harmonics. These shapes (called droplets in the following) can be experimentally tomographed by measuring the expectation values of rotated axial tensor operators. We present an experimental framework for implementing the scanning-based tomography technique for circuit-based quantum computers and showcase results from IBM quantum experience. We also present a method for estimating the density and process matrices from experimentally tomographed Wigner functions (droplets). This tomography approach can be directly implemented using the Python-based software package DROPStomo.